How can companies adopt Artificial Intelligence in 2026?

Why adopt AI?

Artificial intelligence is here now, and its progress is outpacing many people’s expectations. Modern systems are increasingly capable of reasoning, planning, and taking action with remarkable accuracy.

Today, AI can generate software, condense complex legal materials, interrogate and interpret data, and enable organisations to reinvent how work is organised and delivered. Organisations that embrace these tools are already shifting from small experiments to demonstrable, real-world results. The transformation is no longer hypothetical; it is tangible, quantifiable, and gathering speed.

• Increase efficiency and productivity

  • Automates repetitive tasks and streamlines workflows, freeing people for higher-value work. This can reduce cycle times and error rates.​

• Improve decision quality

  • Analyzes large datasets to reveal patterns and actionable insights, enabling data-driven strategy and faster responses to changes.​

• Enhance customer experience

  • AI-powered chatbots and support tools deliver faster, 24/7 assistance, improving satisfaction while reducing support load.​

• Enable cost savings and scalability

  • Automation lowers operating costs and can scale with demand without linear increases in headcount.​

• Level the playing field for smaller firms

  • Access to affordable AI and automation tools helps smaller organizations compete with larger peers.​

Best ways to implement AI

• Start with a clear AI strategy

  • Define objectives (e.g., reduce cycle time by X%, improve forecast accuracy, cut manual work by Y%) and map them to concrete use cases. Align with business outcomes and measurable KPIs.​

• Prioritize high-impact, low-risk use cases

  • Begin with processes that are repetitive, data-rich, and well-defined to demonstrate quick wins and build internal confidence.​

• Build an AI-ready data foundation

  • Ensure data quality, integration, governance, and accessible data platforms so AI models can learn from reliable inputs.​

• Foster an AI-friendly culture

  • Encourage cross-functional collaboration, invest in skills development, and create a governance model to manage AI ethics, risk, and change management.​

• Combine automation with human oversight

  • Use AI to augment human teams, not replace them. Implement human-in-the-loop where critical decisions require nuance and accountability.​

• Measure and iterate

  • Track outcomes with defined metrics, run experiments (A/B tests, pilots), and scale successful pilots while sunsetting underperforming ones.​

• Manage risk and governance

  • Establish data privacy, security controls, bias mitigation, and compliance protocols to address regulatory and ethical considerations.​

Practical starter roadmap

• Quarter 1: Identify 2–3 high-potential use cases; assemble a cross-functional AI team; audit data readiness.

• Quarter 2:Deploy pilot solutions; monitor metrics; adjust data pipelines and governance.

• Quarter 3: Expand successful pilots; begin broader change management; invest in upskilling staff.

• Quarter 4: Scale across functions; formalize AI operating model; set ongoing optimization programs.

Potential pitfalls to avoid

• Overpromising on capabilities and underinvesting in governance and data quality.

• Launching large, multi-process AI programs without clear ownership or change management.

• Neglecting ethics, bias, and privacy considerations in data and model design.